Flock4D (tar.gz format)

This dataset contains the Flock4D dataset converted to the VLBM/Flock4D-compatible format. To facilitate easier downloading and storage, the 1000 sequences have been compressed into .tar.gz archives in chunks of 50 sequences per archive.

Scale

Flock4D provides 1000 sequences of birds (flocks) flying in various environments. There are 24 species of birds included: cannada_goose, common_starling, cormorant, crane, crested_bis, crow, dove, duck, dunlin, eagle, egret, flamingo, jackdaw, mallard, parrot, pelican, pigeon, red_billed_starling, seagull, snow_goose, stork, swallow, tit, warbler.

Dataset Structure

The original dataset is structured by sequence. In this Hugging Face repository, the sequences are grouped and compressed into tarballs (e.g., flock4d_00000_00049.tar.gz). After extracting a .tar.gz archive, each sequence directory follows this layout:

{Species}_{Background}_4k_{ID}/
├── rgbs/
│   ├── rgb_00000.jpg
│   ├── rgb_00001.jpg
│   └── ...
├── depths/
│   ├── depth_00000.npz
│   ├── depth_00001.npz
│   └── ...
├── intrinsics.npy
├── extrinsics.npy
├── trajs_2d.npy
├── trajs_3d.npy
├── visibilities.npy
└── scene_info.json

File Descriptions

• rgbs/: RGB frames saved as JPEG (rgb_XXXXX.jpg).
• depths/: Dense depth maps saved as compressed NumPy archives (depth_XXXXX.npz). Each archive stores a float16 array.
• intrinsics.npy: Camera intrinsic matrices for each frame (T, 3, 3).
• extrinsics.npy: World-to-camera extrinsic matrices (W2C) for each frame (T, 4, 4).
• trajs_2d.npy: 2D trajectories (T, N, 2) -- pixel coordinates (x, y).
• trajs_3d.npy: 3D trajectories (T, N, 3) -- world-space coordinates (x, y, z); zero-filled where invisible.
• visibilities.npy: Visibility flags (T, N) (1.0 visible, 0.0 not visible).
• scene_info.json: JSON file with per-sequence metadata, including camera properties and scene assets.

Usage Example (Python)

To use the dataset, first download the tarballs and extract them:

mkdir -p data/flock4d
tar -xvf flock4d_00000_00049.tar.gz -C data/flock4d/

Then load the annotations in Python:

import numpy as np
from PIL import Image
from pathlib import Path
import json

seq_dir = Path("data/flock4d/cannada_goose_abandoned_parking_4k_313")

# Load annotations
trajs_2d    = np.load(seq_dir / "trajs_2d.npy")     # (T, N, 2)
trajs_3d    = np.load(seq_dir / "trajs_3d.npy")     # (T, N, 3)
vis         = np.load(seq_dir / "visibilities.npy") # (T, N)
intrinsics  = np.load(seq_dir / "intrinsics.npy")   # (T, 3, 3)
extrinsics  = np.load(seq_dir / "extrinsics.npy")   # (T, 4, 4)

# Load context
frame_idx = 0
rgb = Image.open(seq_dir / "rgbs" / f"rgb_{frame_idx:05d}.jpg")
depth_npz = np.load(seq_dir / "depths" / f"depth_{frame_idx:05d}.npz")
depth = depth_npz['depth']  # float16 array (H, W)

# Load scene info
with open(seq_dir / "scene_info.json", 'r') as f:
    scene_info = json.load(f)

print(scene_info)